May 13, 2024, 4:41 a.m. | Jessica N. Howard, Ro Jefferson, Anindita Maiti, Zohar Ringel

cs.LG updates on

arXiv:2405.06008v1 Announce Type: new
Abstract: Separating relevant and irrelevant information is key to any modeling process or scientific inquiry. Theoretical physics offers a powerful tool for achieving this in the form of the renormalization group (RG). Here we demonstrate a practical approach to performing Wilsonian RG in the context of Gaussian Process (GP) Regression. We systematically integrate out the unlearnable modes of the GP kernel, thereby obtaining an RG flow of the Gaussian Process in which the data plays the …

abstract arxiv cond-mat.dis-nn context cs.lg form gaussian processes hep-th information key modeling network neural network physics practical process processes scientific theoretical physics tool type

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